The present invention is directed to the field of computer systems, and more specifically to a method and system of anomaly detection in images and videos.
Anomaly detection is defined as the problem of finding abnormal patterns in the given data. An abnormal pattern is anything that does not conform to normal. It has been introduced and researched upon by various researches in the past few decades. It has been studied by the statistical community for a century. It is very subjective by nature. An anomaly in one case can be completely normal in another case. So, domain knowledge is very important to develop a robust solution for detecting anomalies. The problem is prevalent in various fields such as, surveillance, network security, fraud detection, speech recognition, medical imaging etc and has wide applications.
Most algorithms try to model normal patterns and determine the deviation of the new patterns to the models and use this to classify anomalies. Existing methods vary from full supervision to no supervision. The amount of supervision ranges a lot from Rule-based methods, compared to unsupervised methods that directly learn normal activity patterns.